Comprehensive review of deep learning-based 3d point cloud completion processing and analysis
Point cloud completion is a generation and estimation issue derived from the partial point
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …
A survey of 3d ear recognition techniques
Human recognition with biometrics is a rapidly emerging area of computer vision. Compared
to other well-known biometric features such as the face, fingerprint, iris, and palmprint, the …
to other well-known biometric features such as the face, fingerprint, iris, and palmprint, the …
Pointr: Diverse point cloud completion with geometry-aware transformers
Point clouds captured in real-world applications are often incomplete due to the limited
sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point …
sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point …
Snowflakenet: Point cloud completion by snowflake point deconvolution with skip-transformer
Point cloud completion aims to predict a complete shape in high accuracy from its partial
observation. However, previous methods usually suffered from discrete nature of point cloud …
observation. However, previous methods usually suffered from discrete nature of point cloud …
Seedformer: Patch seeds based point cloud completion with upsample transformer
Point cloud completion has become increasingly popular among generation tasks of 3D
point clouds, as it is a challenging yet indispensable problem to recover the complete shape …
point clouds, as it is a challenging yet indispensable problem to recover the complete shape …
Learning consistency-aware unsigned distance functions progressively from raw point clouds
Surface reconstruction for point clouds is an important task in 3D computer vision. Most of
the latest methods resolve this problem by learning signed distance functions (SDF) from …
the latest methods resolve this problem by learning signed distance functions (SDF) from …
Pmp-net++: Point cloud completion by transformer-enhanced multi-step point moving paths
Point cloud completion concerns to predict missing part for incomplete 3D shapes. A
common strategy is to generate complete shape according to incomplete input. However …
common strategy is to generate complete shape according to incomplete input. However …
Learning a more continuous zero level set in unsigned distance fields through level set projection
Latest methods represent shapes with open surfaces using unsigned distance functions
(UDFs). They train neural networks to learn UDFs and reconstruct surfaces with the …
(UDFs). They train neural networks to learn UDFs and reconstruct surfaces with the …
A conditional point diffusion-refinement paradigm for 3d point cloud completion
3D point cloud is an important 3D representation for capturing real world 3D objects.
However, real-scanned 3D point clouds are often incomplete, and it is important to recover …
However, real-scanned 3D point clouds are often incomplete, and it is important to recover …
Hyperbolic chamfer distance for point cloud completion
Chamfer distance (CD) is a standard metric to measure the shape dissimilarity between
point clouds in point cloud completion, as well as a loss function for (deep) learning …
point clouds in point cloud completion, as well as a loss function for (deep) learning …